Chapter 6 Diversity analysis
6.1 Alpha diversity
# Calculate Hill numbers
richness <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 0) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(richness = 1) %>%
rownames_to_column(var = "sample")
neutral <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(neutral = 1) %>%
rownames_to_column(var = "sample")
phylogenetic <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1, tree = genome_tree) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(phylogenetic = 1) %>%
rownames_to_column(var = "sample")
# Aggregate basal GIFT into elements
dist <- genome_gifts %>%
to.elements(., GIFT_db3) %>%
traits2dist(., method = "gower")
functional <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1, dist = dist) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(functional = 1) %>%
rownames_to_column(var = "sample") %>%
mutate(functional = if_else(is.nan(functional), 1, functional))
# Merge all metrics
alpha_div <- richness %>%
full_join(neutral, by = join_by(sample == sample)) %>%
full_join(phylogenetic, by = join_by(sample == sample)) %>%
full_join(functional, by = join_by(sample == sample))6.1.1 Wild samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.58) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")6.1.2 Acclimation samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.58) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")6.1.3 Post-Transplant_1 samples
bxp1<-alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
scale_x_discrete(labels = c("Control" = "Cold-Cold", "Hot_control" = "Hot-Hot", "Treatment" = "Cold-Hot")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")
# Add significance bars with p-values
bxp1 +
geom_signif(comparisons = list(c("Control", "Hot_control"), c("Control", "Treatment"), c("Hot_control", "Treatment")),
map_signif_level = TRUE)6.1.4 Post-Transplant_2 samples
bxp1<-alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-control","Warm-control", "Cold-intervention"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-control","Warm-control", "Cold-intervention"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
scale_x_discrete(labels = c("Control" = "Cold-Cold", "Hot_control" = "Hot-Hot", "Treatment" = "Cold-Hot")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")
# Add significance bars with p-values
bxp1 +
geom_signif(comparisons = list(c("Control", "Hot_control"), c("Control", "Treatment"), c("Hot_control", "Treatment")),
map_signif_level = TRUE)6.2 Beta diversity
beta_q0n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 0)
beta_q1n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 1)
beta_q1p <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 1, tree = genome_tree)
beta_q1f <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 1, dist = dist)6.3 Permanovas
6.3.1 1. Are the wild populations similar?
6.3.1.2 Richness
richness <- as.matrix(beta_q0n$S)
richness <- as.dist(richness[rownames(richness) %in% samples_to_keep,
colnames(richness) %in% samples_to_keep])
betadisper(richness, subset_meta$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.000012 0.000012 0.0012 999 0.978
Residuals 25 0.257281 0.010291
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.98
Hot_dry 0.97302
adonis2(richness ~ Population,
data = subset_meta %>% arrange(match(Tube_code,labels(richness))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 1 | 1.542719 | 0.2095041 | 6.625717 | 0.001 |
| Residual | 25 | 5.820951 | 0.7904959 | NA | NA |
| Total | 26 | 7.363669 | 1.0000000 | NA | NA |
6.3.1.3 Neutral
neutral <- as.matrix(beta_q1n$S)
neutral <- as.dist(neutral[rownames(neutral) %in% samples_to_keep,
colnames(neutral) %in% samples_to_keep])
betadisper(neutral, subset_meta$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.000048 0.0000476 0.0044 999 0.951
Residuals 25 0.270114 0.0108046
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.954
Hot_dry 0.94763
adonis2(neutral ~ Population,
data = subset_meta %>% arrange(match(Tube_code,labels(neutral))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 1 | 1.918266 | 0.2608511 | 8.822682 | 0.001 |
| Residual | 25 | 5.435610 | 0.7391489 | NA | NA |
| Total | 26 | 7.353876 | 1.0000000 | NA | NA |
6.3.1.4 Phylogenetic
phylo <- as.matrix(beta_q1p$S)
phylo <- as.dist(phylo[rownames(phylo) %in% samples_to_keep,
colnames(phylo) %in% samples_to_keep])
betadisper(phylo, subset_meta$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.03585 0.035847 2.4912 999 0.133
Residuals 25 0.35973 0.014389
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.126
Hot_dry 0.12705
adonis2(phylo ~ Population,
data = subset_meta %>% arrange(match(Tube_code,labels(phylo))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 1 | 0.3218613 | 0.2162815 | 6.899207 | 0.001 |
| Residual | 25 | 1.1662981 | 0.7837185 | NA | NA |
| Total | 26 | 1.4881594 | 1.0000000 | NA | NA |
6.3.1.5 Functional
func <- as.matrix(beta_q1f$S)
func <- as.dist(func[rownames(func) %in% samples_to_keep,
colnames(func) %in% samples_to_keep])
betadisper(func, subset_meta$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.019387 0.019387 1.653 999 0.199
Residuals 25 0.293200 0.011728
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.205
Hot_dry 0.21033
adonis2(func ~ Population,
data = subset_meta %>% arrange(match(Tube_code,labels(func))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 1 | 0.0831048 | 0.1680538 | 5.05002 | 0.067 |
| Residual | 25 | 0.4114083 | 0.8319462 | NA | NA |
| Total | 26 | 0.4945131 | 1.0000000 | NA | NA |
beta_q0n_nmds_wild <- richness %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta, by = join_by(sample == Tube_code))
beta_q1n_nmds_wild <- neutral %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta, by = join_by(sample == Tube_code))
beta_q1p_nmds_wild <- phylo %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta, by = join_by(sample == Tube_code))
beta_q1f_nmds_wild <- func %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta, by = join_by(sample == Tube_code))6.3.2 2. Effect of acclimation
6.3.2.1 Richness
richness_accli <- as.matrix(beta_q0n$S)
richness_accli <- as.dist(richness_accli[rownames(richness_accli) %in% samples_to_keep_accli,
colnames(richness_accli) %in% samples_to_keep_accli])
betadisper(richness_accli, subset_meta_accli$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.093187 0.093187 11.812 999 0.002 **
Residuals 24 0.189340 0.007889
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.002
Hot_dry 0.0021532
adonis2(richness_accli ~ Population,
data = subset_meta_accli %>% arrange(match(Tube_code,labels(richness_accli))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 1 | 1.639630 | 0.1929088 | 5.736415 | 0.001 |
| Residual | 24 | 6.859879 | 0.8070912 | NA | NA |
| Total | 25 | 8.499509 | 1.0000000 | NA | NA |
6.3.2.2 Neutral
neutral_accli <- as.matrix(beta_q1n$S)
neutral_accli <- as.dist(neutral_accli[rownames(neutral_accli) %in% samples_to_keep_accli,
colnames(neutral_accli) %in% samples_to_keep_accli])
betadisper(neutral_accli, subset_meta_accli$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.05603 0.056026 4.1918 999 0.049 *
Residuals 24 0.32077 0.013365
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.051
Hot_dry 0.051717
adonis2(neutral_accli ~ Population,
data = subset_meta_accli %>% arrange(match(Tube_code,labels(neutral_accli))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 1 | 1.961192 | 0.2493171 | 7.970889 | 0.001 |
| Residual | 24 | 5.905063 | 0.7506829 | NA | NA |
| Total | 25 | 7.866255 | 1.0000000 | NA | NA |
6.3.2.3 Phylogenetic
phylo_accli <- as.matrix(beta_q1p$S)
phylo_accli <- as.dist(phylo_accli[rownames(phylo_accli) %in% samples_to_keep_accli,
colnames(phylo_accli) %in% samples_to_keep_accli])
betadisper(phylo_accli, subset_meta_accli$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.03637 0.036365 2.3087 999 0.136
Residuals 24 0.37804 0.015752
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.133
Hot_dry 0.14172
adonis2(phylo_accli ~ Population,
data = subset_meta_accli %>% arrange(match(Tube_code,labels(phylo_accli))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 1 | 0.2113395 | 0.1379515 | 3.84066 | 0.007 |
| Residual | 24 | 1.3206449 | 0.8620485 | NA | NA |
| Total | 25 | 1.5319844 | 1.0000000 | NA | NA |
6.3.2.4 Functional
func_accli <- as.matrix(beta_q1f$S)
func_accli <- as.dist(func_accli[rownames(func_accli) %in% samples_to_keep_accli,
colnames(func_accli) %in% samples_to_keep_accli])
betadisper(func_accli, subset_meta_accli$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00409 0.004087 0.1789 999 0.673
Residuals 24 0.54821 0.022842
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.68
Hot_dry 0.67607
adonis2(func_accli ~ Population,
data = subset_meta_accli %>% arrange(match(Tube_code,labels(func_accli))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 1 | 0.00401769 | 0.005973179 | 0.1442177 | 0.636 |
| Residual | 24 | 0.66860401 | 0.994026821 | NA | NA |
| Total | 25 | 0.67262170 | 1.000000000 | NA | NA |
beta_q0n_nmds_accli <- richness_accli %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_accli, by = join_by(sample == Tube_code))
beta_q1n_nmds_accli <- neutral_accli %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_accli, by = join_by(sample == Tube_code))
beta_q1p_nmds_accli <- phylo_accli %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_accli, by = join_by(sample == Tube_code))
beta_q1f_nmds_accli <- func_accli %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_accli, by = join_by(sample == Tube_code))6.3.3 3. Comparison between Wild and Acclimation
6.3.3.0.2 Richness
richness_accli1 <- as.matrix(beta_q0n$S)
richness_accli1 <- as.dist(richness_accli1[rownames(richness_accli1) %in% samples_to_keep_accli1,
colnames(richness_accli1) %in% samples_to_keep_accli1])
betadisper(richness_accli1, subset_meta_accli1$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.03286 0.032865 2.9698 999 0.097 .
Residuals 51 0.56438 0.011066
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.089
Hot_dry 0.09089
adonis2(richness_accli1 ~ Population*time_point,
data = subset_meta_accli1 %>% arrange(match(Tube_code,labels(richness_accli1))),
permutations = 999,
strata = subset_meta_accli1 %>% arrange(match(Tube_code,labels(richness_accli1))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 3 | 3.762933 | 0.2288365 | 4.846784 | 0.001 |
| Residual | 49 | 12.680830 | 0.7711635 | NA | NA |
| Total | 52 | 16.443763 | 1.0000000 | NA | NA |
#Arrange of metadata dataframe
subset_meta_accli1_arrange <- column_to_rownames(subset_meta_accli1, "Tube_code")
subset_meta_accli1_arrange<-subset_meta_accli1_arrange[labels(richness_accli1),]
pairwise<-pairwise.adonis(richness_accli1,subset_meta_accli1_arrange$Population_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation | 1 | 1.6396299 | 5.736415 | 0.19290877 | 0.001 | 0.006 | * |
| Cold_wet.1_Acclimation vs Cold_wet.0_Wild | 1 | 0.5273732 | 1.906862 | 0.05462715 | 0.008 | 0.048 | . |
| Cold_wet.1_Acclimation vs Hot_dry.0_Wild | 1 | 1.5558412 | 5.190707 | 0.17782052 | 0.001 | 0.006 | * |
| Hot_dry.1_Acclimation vs Cold_wet.0_Wild | 1 | 1.8259388 | 8.319131 | 0.24968031 | 0.001 | 0.006 | * |
| Hot_dry.1_Acclimation vs Hot_dry.0_Wild | 1 | 0.3856333 | 1.736034 | 0.09788177 | 0.008 | 0.048 | . |
| Cold_wet.0_Wild vs Hot_dry.0_Wild | 1 | 1.5427188 | 6.625717 | 0.20950408 | 0.001 | 0.006 | * |
6.3.3.0.3 Neutral
neutral_accli1 <- as.matrix(beta_q1n$S)
neutral_accli1 <- as.dist(neutral_accli1[rownames(neutral_accli1) %in% samples_to_keep_accli1,
colnames(neutral_accli1) %in% samples_to_keep_accli1])
betadisper(neutral_accli1, subset_meta_accli1$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01431 0.014310 1.1177 999 0.324
Residuals 51 0.65296 0.012803
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.312
Hot_dry 0.29539
adonis2(neutral_accli1 ~ Population*time_point,
data = subset_meta_accli1 %>% arrange(match(Tube_code,labels(neutral_accli1))),
permutations = 999,
strata = subset_meta_accli1 %>% arrange(match(Tube_code,labels(neutral_accli1))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 3 | 4.735485 | 0.2945657 | 6.820252 | 0.001 |
| Residual | 49 | 11.340673 | 0.7054343 | NA | NA |
| Total | 52 | 16.076158 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(neutral_accli1,subset_meta_accli1_arrange$Population_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation | 1 | 1.9611917 | 7.970889 | 0.24931708 | 0.001 | 0.006 | * |
| Cold_wet.1_Acclimation vs Cold_wet.0_Wild | 1 | 0.7466371 | 3.064914 | 0.08498327 | 0.001 | 0.006 | * |
| Cold_wet.1_Acclimation vs Hot_dry.0_Wild | 1 | 2.1421077 | 8.176866 | 0.25412251 | 0.001 | 0.006 | * |
| Hot_dry.1_Acclimation vs Cold_wet.0_Wild | 1 | 2.0371666 | 10.078295 | 0.28730857 | 0.001 | 0.006 | * |
| Hot_dry.1_Acclimation vs Hot_dry.0_Wild | 1 | 0.6314393 | 3.060027 | 0.16054681 | 0.001 | 0.006 | * |
| Cold_wet.0_Wild vs Hot_dry.0_Wild | 1 | 1.9182663 | 8.822682 | 0.26085105 | 0.001 | 0.006 | * |
6.3.3.0.4 Phylogenetic
phylo_accli1 <- as.matrix(beta_q1p$S)
phylo_accli1 <- as.dist(phylo_accli1[rownames(phylo_accli1) %in% samples_to_keep_accli1,
colnames(phylo_accli1) %in% samples_to_keep_accli1])
betadisper(phylo_accli1, subset_meta_accli1$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00001 0.0000111 6e-04 999 0.981
Residuals 51 0.89017 0.0174543
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.982
Hot_dry 0.97998
adonis2(phylo_accli1 ~ Population*time_point,
data = subset_meta_accli1 %>% arrange(match(Tube_code,labels(phylo_accli1))),
permutations = 999,
strata = subset_meta_accli1 %>% arrange(match(Tube_code,labels(phylo_accli1))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 3 | 0.7622566 | 0.2345983 | 5.006223 | 0.001 |
| Residual | 49 | 2.4869430 | 0.7654017 | NA | NA |
| Total | 52 | 3.2491997 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(phylo_accli1,subset_meta_accli1_arrange$Population_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation | 1 | 0.2113395 | 3.840660 | 0.1379515 | 0.011 | 0.066 | |
| Cold_wet.1_Acclimation vs Cold_wet.0_Wild | 1 | 0.2076830 | 4.216762 | 0.1133028 | 0.002 | 0.012 | . |
| Cold_wet.1_Acclimation vs Hot_dry.0_Wild | 1 | 0.3635093 | 4.799786 | 0.1666605 | 0.001 | 0.006 | * |
| Hot_dry.1_Acclimation vs Cold_wet.0_Wild | 1 | 0.2121479 | 7.924059 | 0.2406769 | 0.002 | 0.012 | . |
| Hot_dry.1_Acclimation vs Hot_dry.0_Wild | 1 | 0.2092433 | 3.885515 | 0.1953942 | 0.001 | 0.006 | * |
| Cold_wet.0_Wild vs Hot_dry.0_Wild | 1 | 0.3218613 | 6.899207 | 0.2162815 | 0.001 | 0.006 | * |
6.3.3.0.5 Functional
func_accli1 <- as.matrix(beta_q1f$S)
func_accli1 <- as.dist(func_accli1[rownames(func_accli1) %in% samples_to_keep_accli1,
colnames(func_accli1) %in% samples_to_keep_accli1])
betadisper(func_accli1, subset_meta_accli1$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01374 0.013738 0.7923 999 0.399
Residuals 51 0.88435 0.017340
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.4
Hot_dry 0.3776
adonis2(func_accli1 ~ Population*time_point,
data = subset_meta_accli1 %>% arrange(match(Tube_code,labels(func_accli1))),
permutations = 999,
strata = subset_meta_accli1 %>% arrange(match(Tube_code,labels(func_accli1))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 3 | 0.1024301 | 0.08662587 | 1.54908 | 0.194 |
| Residual | 49 | 1.0800123 | 0.91337413 | NA | NA |
| Total | 52 | 1.1824424 | 1.00000000 | NA | NA |
pairwise<-pairwise.adonis(func_accli1,subset_meta_accli1_arrange$Population_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation | 1 | 0.0040176901 | 0.144217742 | 0.0059731793 | 0.634 | 1.000 | |
| Cold_wet.1_Acclimation vs Cold_wet.0_Wild | 1 | 0.0002775823 | 0.009969841 | 0.0003020251 | 0.704 | 1.000 | |
| Cold_wet.1_Acclimation vs Hot_dry.0_Wild | 1 | 0.0871206378 | 3.762920746 | 0.1355376396 | 0.079 | 0.474 | |
| Hot_dry.1_Acclimation vs Cold_wet.0_Wild | 1 | 0.0025235151 | 0.120315257 | 0.0047895600 | 0.606 | 1.000 | |
| Hot_dry.1_Acclimation vs Hot_dry.0_Wild | 1 | 0.0409732757 | 4.066330477 | 0.2026444487 | 0.092 | 0.552 | |
| Cold_wet.0_Wild vs Hot_dry.0_Wild | 1 | 0.0831047968 | 5.050019518 | 0.1680537849 | 0.060 | 0.360 |
beta_richness_nmds_accli1 <- richness_accli1 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_accli1, by = c("sample" = "Tube_code"))
beta_neutral_nmds_accli1 <- neutral_accli1 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_accli1, by = c("sample" = "Tube_code"))
beta_phylo_nmds_accli1 <- phylo_accli1 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_accli1, by = join_by(sample == Tube_code))
beta_func_nmds_accli1 <- func_accli1 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_accli1, by = join_by(sample == Tube_code))6.3.4 4. Effect of FMT on microbiota community
6.3.4.1 Comparison between Acclimation vs Post-FMT1
6.3.4.1.2 Richness
richness_post3 <- as.matrix(beta_q0n$S)
richness_post3 <- as.dist(richness_post3[rownames(richness_post3) %in% samples_to_keep_post3,
colnames(richness_post3) %in% samples_to_keep_post3])
betadisper(richness_post3, subset_meta_post3$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.10843 0.108427 25.578 999 0.001 ***
Residuals 50 0.21195 0.004239
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.001
Hot_dry 6.0928e-06
adonis2(richness_post3 ~ Population*time_point,
data = subset_meta_post3 %>% arrange(match(Tube_code,labels(richness_post3))),
permutations = 999,
strata = subset_meta_post3 %>% arrange(match(Tube_code,labels(richness_post3))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 3 | 3.472191 | 0.193401 | 3.836375 | 0.001 |
| Residual | 48 | 14.481131 | 0.806599 | NA | NA |
| Total | 51 | 17.953321 | 1.000000 | NA | NA |
#Arrange of metadata dataframe
subset_meta_post3_arrange <- column_to_rownames(subset_meta_post3, "Tube_code")
subset_meta_post3_arrange<-subset_meta_post3_arrange[labels(richness_post3),]
pairwise <- pairwise.adonis(richness_post3, subset_meta_post3_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.3657243 | 1.123239 | 0.06966584 | 0.235 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.2605860 | 4.943717 | 0.24788342 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.6804988 | 2.100611 | 0.12283837 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.8481258 | 2.673290 | 0.16033371 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.1180661 | 3.809275 | 0.20252111 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.3606630 | 5.087152 | 0.24124415 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.7216200 | 2.172734 | 0.11956009 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.9551308 | 2.926054 | 0.16322910 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.2263345 | 4.039487 | 0.20157637 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 1.4319792 | 5.384836 | 0.25180628 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.8172413 | 3.194690 | 0.17558364 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.5796135 | 2.441615 | 0.13239702 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.5615418 | 1.729004 | 0.10335366 | 0.014 | 0.210 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.8438429 | 2.793772 | 0.14865413 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.3734921 | 1.268929 | 0.07799710 | 0.100 | 1.000 |
6.3.4.1.3 Neutral
neutral_post3 <- as.matrix(beta_q1n$S)
neutral_post3 <- as.dist(neutral_post3[rownames(neutral_post3) %in% samples_to_keep_post3,
colnames(neutral_post3) %in% samples_to_keep_post3])
betadisper(neutral_post3, subset_meta_post3$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.09089 0.090889 12.898 999 0.002 **
Residuals 50 0.35233 0.007047
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.001
Hot_dry 0.00074926
adonis2(neutral_post3 ~ Population*time_point,
data = subset_meta_post3 %>% arrange(match(Tube_code,labels(neutral_post3))),
permutations = 999,
strata = subset_meta_post3 %>% arrange(match(Tube_code,labels(neutral_post3))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 3 | 4.443476 | 0.2628643 | 5.705637 | 0.001 |
| Residual | 48 | 12.460591 | 0.7371357 | NA | NA |
| Total | 51 | 16.904067 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(neutral_post3, subset_meta_post3_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.2759858 | 0.9928976 | 0.06208366 | 0.457 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.4146464 | 6.5078325 | 0.30257965 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.8153894 | 3.0970603 | 0.17113610 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 1.1809241 | 4.4856470 | 0.24265567 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.4321524 | 5.7774260 | 0.27806264 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.6347704 | 6.8326887 | 0.29925029 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.9517634 | 3.3715700 | 0.17404733 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 1.3127773 | 4.6298256 | 0.23585668 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.6713369 | 6.2395460 | 0.28056085 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 1.5409781 | 6.8338056 | 0.29928456 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.9133614 | 4.0964534 | 0.21451383 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.6954835 | 3.2951234 | 0.17077493 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.6051778 | 2.2508491 | 0.13047758 | 0.016 | 0.240 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 1.0528902 | 4.1436369 | 0.20570451 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.4150076 | 1.6372683 | 0.09840968 | 0.058 | 0.870 |
6.3.4.1.4 Phylogenetic
phylo_post3 <- as.matrix(beta_q1p$S)
phylo_post3 <- as.dist(phylo_post3[rownames(phylo_post3) %in% samples_to_keep_post3,
colnames(phylo_post3) %in% samples_to_keep_post3])
betadisper(phylo_post3, subset_meta_post3$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.04088 0.040882 2.9254 999 0.102
Residuals 50 0.69874 0.013975
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.093
Hot_dry 0.093394
adonis2(phylo_post3 ~ Population*time_point,
data = subset_meta_post3 %>% arrange(match(Tube_code,labels(phylo_post3))),
permutations = 999,
strata = subset_meta_post3 %>% arrange(match(Tube_code,labels(phylo_post3))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 3 | 0.6468577 | 0.2180259 | 4.461035 | 0.004 |
| Residual | 48 | 2.3200272 | 0.7819741 | NA | NA |
| Total | 51 | 2.9668849 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(phylo_post3, subset_meta_post3_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.05690611 | 0.7893300 | 0.04999136 | 0.559 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.10866997 | 3.0547314 | 0.16919285 | 0.010 | 0.150 | |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.11760287 | 2.9988032 | 0.16661126 | 0.018 | 0.270 | |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.09624422 | 1.7247115 | 0.10968160 | 0.134 | 1.000 | |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.18586788 | 4.1904227 | 0.21836011 | 0.004 | 0.060 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.0521838 | 0.20208192 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.26358465 | 4.3608960 | 0.21417997 | 0.005 | 0.075 | |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.25319427 | 3.2738422 | 0.17915456 | 0.045 | 0.675 | |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.39050120 | 5.9837393 | 0.27218933 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.14203376 | 5.4200212 | 0.25303529 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.09666753 | 2.3682173 | 0.13635351 | 0.020 | 0.300 | |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.09252600 | 2.9824958 | 0.15711821 | 0.006 | 0.090 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.01842535 | 0.4144162 | 0.02688498 | 0.774 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.05987967 | 1.7387847 | 0.09802164 | 0.095 | 1.000 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.03212966 | 0.6477782 | 0.04139746 | 0.708 | 1.000 |
6.3.4.1.5 Functional
func_post3 <- as.matrix(beta_q1f$S)
func_post3 <- as.dist(func_post3[rownames(func_post3) %in% samples_to_keep_post3,
colnames(func_post3) %in% samples_to_keep_post3])
betadisper(func_post3, subset_meta_post3$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00116 0.0011552 0.0615 999 0.798
Residuals 50 0.93946 0.0187892
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.791
Hot_dry 0.80518
adonis2(func_post3 ~ Population*time_point,
data = subset_meta_post3 %>% arrange(match(Tube_code,labels(func_post3))),
permutations = 999,
strata = subset_meta_post3 %>% arrange(match(Tube_code,labels(func_post3))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 3 | 0.02861503 | 0.01961684 | 0.3201497 | 0.424 |
| Residual | 48 | 1.43008228 | 0.98038316 | NA | NA |
| Total | 51 | 1.45869731 | 1.00000000 | NA | NA |
pairwise <- pairwise.adonis(func_post3, subset_meta_post3_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.022076664 | 0.65002192 | 0.041534889 | 0.400 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.016749605 | 0.52091146 | 0.033561912 | 0.475 | 1.000 | |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | -0.008325555 | -0.22800110 | -0.015434681 | 0.902 | 1.000 | |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.126527918 | 3.45550519 | 0.197960767 | 0.065 | 0.975 | |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.051008429 | 1.23951838 | 0.076327287 | 0.316 | 1.000 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.001022590 | 0.05430389 | 0.003382513 | 0.639 | 1.000 | |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.002157067 | 0.09411569 | 0.005847832 | 0.619 | 1.000 | |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.056602363 | 2.56037069 | 0.145803909 | 0.148 | 1.000 | |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.009569124 | 0.35095521 | 0.021463896 | 0.506 | 1.000 | |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | -0.001745663 | -0.08225018 | -0.005167199 | 0.703 | 1.000 | |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.057758674 | 2.84545622 | 0.159449901 | 0.157 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.005575266 | 0.21803560 | 0.013444020 | 0.557 | 1.000 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.119540855 | 4.84764704 | 0.244242909 | 0.076 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.052587837 | 1.77308932 | 0.099762584 | 0.212 | 1.000 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.012980354 | 0.44307662 | 0.028690955 | 0.464 | 1.000 |
beta_richness_nmds_post3 <- richness_post3 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post3, by = c("sample" = "Tube_code"))
beta_neutral_nmds_post3 <- neutral_post3 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post3, by = c("sample" = "Tube_code"))
beta_phylo_nmds_post3 <- phylo_post3 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post3, by = join_by(sample == Tube_code))
beta_func_nmds_post3 <- func_post3 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post3, by = join_by(sample == Tube_code))6.3.4.2 Comparison between Acclimation vs Post-FMT2
6.3.4.2.2 Richness
richness_post4 <- as.matrix(beta_q0n$S)
richness_post4 <- as.dist(richness_post4[rownames(richness_post4) %in% samples_to_keep_post4,
colnames(richness_post4) %in% samples_to_keep_post4])
betadisper(richness_post4, subset_meta_post4$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.07832 0.078322 11.371 999 0.001 ***
Residuals 51 0.35129 0.006888
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.001
Hot_dry 0.0014303
adonis2(richness_post4 ~ Population*time_point,
data = subset_meta_post4 %>% arrange(match(Tube_code,labels(richness_post4))),
permutations = 999,
strata = subset_meta_post4 %>% arrange(match(Tube_code,labels(richness_post4))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 3 | 3.279021 | 0.1937289 | 3.924535 | 0.001 |
| Residual | 49 | 13.646799 | 0.8062711 | NA | NA |
| Total | 52 | 16.925820 | 1.0000000 | NA | NA |
#Arrange of metadata dataframe
subset_meta_post4_arrange <- column_to_rownames(subset_meta_post4, "Tube_code")
subset_meta_post4_arrange<-subset_meta_post4_arrange[labels(richness_post4),]
pairwise <- pairwise.adonis(richness_post4, subset_meta_post4_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.3657243 | 1.123239 | 0.06966584 | 0.246 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.2605860 | 4.943717 | 0.24788342 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.8072604 | 2.940901 | 0.16392161 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.4817387 | 1.660775 | 0.09968176 | 0.028 | 0.420 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.1179704 | 3.885459 | 0.20573812 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.3606630 | 5.087152 | 0.24124415 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.9130048 | 3.195028 | 0.16645080 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.5959230 | 1.984036 | 0.11032208 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.2747787 | 4.275366 | 0.21086503 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.6397330 | 2.913695 | 0.15405213 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.4575447 | 6.224524 | 0.28007456 | 0.002 | 0.030 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.3276169 | 1.412318 | 0.08111028 | 0.037 | 0.555 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.6463814 | 2.560441 | 0.13795154 | 0.001 | 0.015 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.4796256 | 1.916520 | 0.10696943 | 0.001 | 0.015 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.1305044 | 4.268317 | 0.21059061 | 0.001 | 0.015 | . |
6.3.4.2.3 Neutral
neutral_post4 <- as.matrix(beta_q1n$S)
neutral_post4 <- as.dist(neutral_post4[rownames(neutral_post4) %in% samples_to_keep_post4,
colnames(neutral_post4) %in% samples_to_keep_post4])
betadisper(neutral_post4, subset_meta_post4$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.07811 0.078108 9.6342 999 0.001 ***
Residuals 51 0.41347 0.008107
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.002
Hot_dry 0.0031133
adonis2(neutral_post4 ~ Population*time_point,
data = subset_meta_post4 %>% arrange(match(Tube_code,labels(neutral_post4))),
permutations = 999,
strata = subset_meta_post4 %>% arrange(match(Tube_code,labels(neutral_post4))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 3 | 3.823089 | 0.2357666 | 5.038847 | 0.001 |
| Residual | 49 | 12.392477 | 0.7642334 | NA | NA |
| Total | 52 | 16.215567 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(neutral_post4, subset_meta_post4_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.2759858 | 0.9928976 | 0.06208366 | 0.453 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.4146464 | 6.5078325 | 0.30257965 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.1524353 | 4.9536068 | 0.24825621 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.4748999 | 1.9609749 | 0.11561688 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.3503168 | 5.4081420 | 0.26499923 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.6347704 | 6.8326887 | 0.29925029 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.3540292 | 5.3398081 | 0.25022756 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.6311089 | 2.4041625 | 0.13063146 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.6125755 | 5.9825981 | 0.27215155 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.6202327 | 3.1519868 | 0.16457754 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.5701179 | 7.6327037 | 0.32297209 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.3634438 | 1.7083388 | 0.09647087 | 0.035 | 0.525 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 1.0227481 | 4.6483346 | 0.22511910 | 0.001 | 0.015 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.5010202 | 2.2065321 | 0.12119453 | 0.001 | 0.015 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.3619424 | 5.7710313 | 0.26507845 | 0.001 | 0.015 | . |
6.3.4.2.4 Phylogenetic
phylo_post4 <- as.matrix(beta_q1p$S)
phylo_post4 <- as.dist(phylo_post4[rownames(phylo_post4) %in% samples_to_keep_post4,
colnames(phylo_post4) %in% samples_to_keep_post4])
betadisper(phylo_post4, subset_meta_post4$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.03098 0.030984 2.885 999 0.089 .
Residuals 51 0.54772 0.010740
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.08
Hot_dry 0.0955
adonis2(phylo_post4 ~ Population*time_point,
data = subset_meta_post4 %>% arrange(match(Tube_code,labels(phylo_post4))),
permutations = 999,
strata = subset_meta_post4 %>% arrange(match(Tube_code,labels(phylo_post4))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 3 | 0.6466392 | 0.2442162 | 5.277784 | 0.001 |
| Residual | 49 | 2.0011759 | 0.7557838 | NA | NA |
| Total | 52 | 2.6478151 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(phylo_post4, subset_meta_post4_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.05690611 | 0.789330 | 0.04999136 | 0.542 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.10866997 | 3.054731 | 0.16919285 | 0.010 | 0.150 | |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.15591805 | 4.379209 | 0.22597458 | 0.006 | 0.090 | |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.07099742 | 1.879364 | 0.11134092 | 0.111 | 1.000 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.18682367 | 4.878754 | 0.24542555 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.052184 | 0.20208192 | 0.007 | 0.105 | |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.36496892 | 6.396667 | 0.28560797 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.22628210 | 3.829222 | 0.19311005 | 0.024 | 0.360 | |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.34830814 | 5.846334 | 0.26761166 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.10002871 | 4.383624 | 0.21505615 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.12577510 | 5.060129 | 0.24027055 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.06334378 | 2.499774 | 0.13512455 | 0.022 | 0.330 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.05927454 | 2.382025 | 0.12958449 | 0.021 | 0.315 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.06906280 | 2.722460 | 0.14541146 | 0.005 | 0.075 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.11081709 | 4.043656 | 0.20174244 | 0.001 | 0.015 | . |
6.3.4.2.5 Functional
func_post4 <- as.matrix(beta_q1f$S)
func_post4 <- as.dist(func_post4[rownames(func_post4) %in% samples_to_keep_post4,
colnames(func_post4) %in% samples_to_keep_post4])
betadisper(func_post4, subset_meta_post4$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00006 0.0000601 0.003 999 0.966
Residuals 51 1.03808 0.0203544
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.965
Hot_dry 0.95688
adonis2(func_post4 ~ Population*time_point,
data = subset_meta_post4 %>% arrange(match(Tube_code,labels(func_post4))),
permutations = 999,
strata = subset_meta_post4 %>% arrange(match(Tube_code,labels(func_post4))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 3 | 0.02585374 | 0.01884827 | 0.313769 | 0.537 |
| Residual | 49 | 1.34582345 | 0.98115173 | NA | NA |
| Total | 52 | 1.37167719 | 1.00000000 | NA | NA |
pairwise <- pairwise.adonis(func_post4, subset_meta_post4_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.0220766636 | 0.650021923 | 0.0415348890 | 0.439 | 1 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.0167496054 | 0.520911459 | 0.0335619117 | 0.457 | 1 | |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.0376809680 | 1.119650309 | 0.0694587220 | 0.298 | 1 | |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.0292200956 | 0.920511083 | 0.0578191917 | 0.386 | 1 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.0133433458 | 0.270473784 | 0.0177122064 | 0.496 | 1 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.0010225901 | 0.054303886 | 0.0033825127 | 0.676 | 1 | |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | -0.0005177706 | -0.025585400 | -0.0016016487 | 0.724 | 1 | |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.0013301207 | 0.072110871 | 0.0044867082 | 0.610 | 1 | |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.0060959077 | 0.174487757 | 0.0107878382 | 0.576 | 1 | |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.0010345754 | 0.055797964 | 0.0034752533 | 0.629 | 1 | |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | -0.0001056284 | -0.006306177 | -0.0003942915 | 0.706 | 1 | |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.0017235602 | 0.051851181 | 0.0032302306 | 0.773 | 1 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | -0.0080428882 | -0.442986255 | -0.0284750185 | 0.848 | 1 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | -0.0011796256 | -0.034047378 | -0.0021324990 | 0.889 | 1 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.0036300838 | 0.110487573 | 0.0068581148 | 0.706 | 1 |
beta_richness_nmds_post4 <- richness_post4 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post4, by = c("sample" = "Tube_code"))
beta_neutral_nmds_post4 <- neutral_post4 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post4, by = c("sample" = "Tube_code"))
beta_phylo_nmds_post4 <- phylo_post4 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post4, by = join_by(sample == Tube_code))
beta_func_nmds_post4 <- func_post4 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post4, by = join_by(sample == Tube_code))6.3.4.3 Comparison between Acclimation vs Post-FMT1 and Post-FMT2
6.3.4.3.2 Richness
richness_post6 <- as.matrix(beta_q0n$S)
richness_post6 <- as.dist(richness_post6[rownames(richness_post6) %in% samples_to_keep_post6,
colnames(richness_post6) %in% samples_to_keep_post6])
betadisper(richness_post6, subset_meta_post6$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.09792 0.097923 18.607 999 0.002 **
Residuals 77 0.40523 0.005263
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.001
Hot_dry 4.714e-05
adonis2(richness_post6 ~ type*time_point,
data = subset_meta_post6 %>% arrange(match(Tube_code,labels(richness_post6))),
permutations = 999,
strata = subset_meta_post6 %>% arrange(match(Tube_code,labels(richness_post6))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 8 | 6.395467 | 0.2451322 | 2.841433 | 0.001 |
| Residual | 70 | 19.694403 | 0.7548678 | NA | NA |
| Total | 78 | 26.089870 | 1.0000000 | NA | NA |
#Arrange of metadata dataframe
subset_meta_post6_arrange <- column_to_rownames(subset_meta_post6, "Tube_code")
subset_meta_post6_arrange<-subset_meta_post6_arrange[labels(richness_post6),]
pairwise <- pairwise.adonis(richness_post6, subset_meta_post6_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.3657243 | 1.123239 | 0.06966584 | 0.248 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.2605860 | 4.943717 | 0.24788342 | 0.001 | 0.036 | . |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.6804988 | 2.100611 | 0.12283837 | 0.001 | 0.036 | . |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.8481258 | 2.673290 | 0.16033371 | 0.001 | 0.036 | . |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.1180661 | 3.809275 | 0.20252111 | 0.001 | 0.036 | . |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.8072604 | 2.940901 | 0.16392161 | 0.001 | 0.036 | . |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.4817387 | 1.660775 | 0.09968176 | 0.026 | 0.936 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.1179704 | 3.885459 | 0.20573812 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.3606630 | 5.087152 | 0.24124415 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.7216200 | 2.172734 | 0.11956009 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.9551308 | 2.926054 | 0.16322910 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.2263345 | 4.039487 | 0.20157637 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.9130048 | 3.195028 | 0.16645080 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.5959230 | 1.984036 | 0.11032208 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.2747787 | 4.275366 | 0.21086503 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 1.4319792 | 5.384836 | 0.25180628 | 0.002 | 0.072 | |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.8172413 | 3.194690 | 0.17558364 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.5796135 | 2.441615 | 0.13239702 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.6397330 | 2.913695 | 0.15405213 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.4575447 | 6.224524 | 0.28007456 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.3276169 | 1.412318 | 0.08111028 | 0.038 | 1.000 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.5615418 | 1.729004 | 0.10335366 | 0.010 | 0.360 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.8438429 | 2.793772 | 0.14865413 | 0.001 | 0.036 | . |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.7628135 | 2.683925 | 0.14364890 | 0.002 | 0.072 | |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.3432605 | 1.148733 | 0.06698647 | 0.236 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 1.1269580 | 3.799256 | 0.19188884 | 0.001 | 0.036 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.3734921 | 1.268929 | 0.07799710 | 0.122 | 1.000 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.3571397 | 1.297184 | 0.07959561 | 0.139 | 1.000 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.7769467 | 2.670898 | 0.15114670 | 0.001 | 0.036 | . |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.6502360 | 2.253407 | 0.13060650 | 0.002 | 0.072 | |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.4132091 | 1.616138 | 0.09174188 | 0.013 | 0.468 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 1.0163992 | 3.760571 | 0.19030682 | 0.001 | 0.036 | . |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.2732563 | 1.019281 | 0.05988979 | 0.416 | 1.000 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.6463814 | 2.560441 | 0.13795154 | 0.001 | 0.036 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.4796256 | 1.916520 | 0.10696943 | 0.001 | 0.036 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.1305044 | 4.268317 | 0.21059061 | 0.001 | 0.036 | . |
6.3.4.3.3 Neutral
neutral_post6 <- as.matrix(beta_q1n$S)
neutral_post6 <- as.dist(neutral_post6[rownames(neutral_post6) %in% samples_to_keep_post6,
colnames(neutral_post6) %in% samples_to_keep_post6])
betadisper(neutral_post6, subset_meta_post6$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.09524 0.095237 15.396 999 0.001 ***
Residuals 77 0.47632 0.006186
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.001
Hot_dry 0.00018818
adonis2(neutral_post6 ~ type*time_point,
data = subset_meta_post6 %>% arrange(match(Tube_code,labels(neutral_post6))),
permutations = 999,
strata = subset_meta_post6 %>% arrange(match(Tube_code,labels(neutral_post6))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 8 | 7.736408 | 0.3121974 | 3.971673 | 0.001 |
| Residual | 70 | 17.044094 | 0.6878026 | NA | NA |
| Total | 78 | 24.780501 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(neutral_post6, subset_meta_post6_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.2759858 | 0.9928976 | 0.06208366 | 0.460 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.4146464 | 6.5078325 | 0.30257965 | 0.001 | 0.036 | . |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.8153894 | 3.0970603 | 0.17113610 | 0.001 | 0.036 | . |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 1.1809241 | 4.4856470 | 0.24265567 | 0.001 | 0.036 | . |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.4321524 | 5.7774260 | 0.27806264 | 0.001 | 0.036 | . |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.1524353 | 4.9536068 | 0.24825621 | 0.001 | 0.036 | . |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.4748999 | 1.9609749 | 0.11561688 | 0.006 | 0.216 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.3503168 | 5.4081420 | 0.26499923 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.6347704 | 6.8326887 | 0.29925029 | 0.002 | 0.072 | |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.9517634 | 3.3715700 | 0.17404733 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 1.3127773 | 4.6298256 | 0.23585668 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.6713369 | 6.2395460 | 0.28056085 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.3540292 | 5.3398081 | 0.25022756 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.6311089 | 2.4041625 | 0.13063146 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.6125755 | 5.9825981 | 0.27215155 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 1.5409781 | 6.8338056 | 0.29928456 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.9133614 | 4.0964534 | 0.21451383 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.6954835 | 3.2951234 | 0.17077493 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.6202327 | 3.1519868 | 0.16457754 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.5701179 | 7.6327037 | 0.32297209 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.3634438 | 1.7083388 | 0.09647087 | 0.035 | 1.000 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.6051778 | 2.2508491 | 0.13047758 | 0.013 | 0.468 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 1.0528902 | 4.1436369 | 0.20570451 | 0.001 | 0.036 | . |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.8908158 | 3.7146920 | 0.18842252 | 0.001 | 0.036 | . |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.3860927 | 1.5521758 | 0.08843210 | 0.081 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 1.3122237 | 5.1302726 | 0.24279254 | 0.001 | 0.036 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.4150076 | 1.6372683 | 0.09840968 | 0.061 | 1.000 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.3157079 | 1.3252026 | 0.08117526 | 0.157 | 1.000 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 1.0579520 | 4.2700097 | 0.22158835 | 0.001 | 0.036 | . |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.7454015 | 2.9200493 | 0.16294873 | 0.002 | 0.072 | |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.4377161 | 1.9421261 | 0.10824392 | 0.003 | 0.108 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 1.3766597 | 5.8752789 | 0.26858075 | 0.001 | 0.036 | . |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.3176516 | 1.3161367 | 0.07600637 | 0.184 | 1.000 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 1.0227481 | 4.6483346 | 0.22511910 | 0.001 | 0.036 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.5010202 | 2.2065321 | 0.12119453 | 0.002 | 0.072 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.3619424 | 5.7710313 | 0.26507845 | 0.001 | 0.036 | . |
6.3.4.3.4 Phylogenetic
phylo_post6 <- as.matrix(beta_q1p$S)
phylo_post6 <- as.dist(phylo_post6[rownames(phylo_post6) %in% samples_to_keep_post6,
colnames(phylo_post6) %in% samples_to_keep_post6])
betadisper(phylo_post6, subset_meta_post6$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.02554 0.025541 2.1633 999 0.153
Residuals 77 0.90909 0.011806
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.153
Hot_dry 0.14542
adonis2(phylo_post6 ~ type*time_point,
data = subset_meta_post6 %>% arrange(match(Tube_code,labels(phylo_post6))),
permutations = 999,
strata = subset_meta_post6 %>% arrange(match(Tube_code,labels(phylo_post6))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 8 | 1.055866 | 0.2692287 | 3.223651 | 0.001 |
| Residual | 70 | 2.865952 | 0.7307713 | NA | NA |
| Total | 78 | 3.921818 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(phylo_post6, subset_meta_post6_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.05690611 | 0.7893300 | 0.04999136 | 0.506 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.10866997 | 3.0547314 | 0.16919285 | 0.012 | 0.432 | |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.11760287 | 2.9988032 | 0.16661126 | 0.022 | 0.792 | |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.09624422 | 1.7247115 | 0.10968160 | 0.161 | 1.000 | |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.18586788 | 4.1904227 | 0.21836011 | 0.006 | 0.216 | |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.15591805 | 4.3792085 | 0.22597458 | 0.002 | 0.072 | |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.07099742 | 1.8793639 | 0.11134092 | 0.101 | 1.000 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.18682367 | 4.8787543 | 0.24542555 | 0.001 | 0.036 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.0521838 | 0.20208192 | 0.005 | 0.180 | |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.26358465 | 4.3608960 | 0.21417997 | 0.009 | 0.324 | |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.25319427 | 3.2738422 | 0.17915456 | 0.041 | 1.000 | |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.39050120 | 5.9837393 | 0.27218933 | 0.002 | 0.072 | |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.36496892 | 6.3966666 | 0.28560797 | 0.002 | 0.072 | |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.22628210 | 3.8292220 | 0.19311005 | 0.019 | 0.684 | |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.34830814 | 5.8463335 | 0.26761166 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.14203376 | 5.4200212 | 0.25303529 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.09666753 | 2.3682173 | 0.13635351 | 0.019 | 0.684 | |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.09252600 | 2.9824958 | 0.15711821 | 0.009 | 0.324 | |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.10002871 | 4.3836237 | 0.21505615 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.12577510 | 5.0601287 | 0.24027055 | 0.001 | 0.036 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.06334378 | 2.4997737 | 0.13512455 | 0.015 | 0.540 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.01842535 | 0.4144162 | 0.02688498 | 0.791 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.05987967 | 1.7387847 | 0.09802164 | 0.107 | 1.000 | |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.07917244 | 3.0180046 | 0.15869197 | 0.010 | 0.360 | |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.04335491 | 1.5335604 | 0.08746429 | 0.179 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.10783045 | 3.7500438 | 0.18987521 | 0.001 | 0.036 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.03212966 | 0.6477782 | 0.04139746 | 0.707 | 1.000 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.06393539 | 1.5651817 | 0.09448624 | 0.147 | 1.000 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.05265949 | 1.2240203 | 0.07544494 | 0.293 | 1.000 | |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.09753501 | 2.2402429 | 0.12994265 | 0.012 | 0.432 | |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.07228545 | 2.3279593 | 0.12701683 | 0.036 | 1.000 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.11759094 | 3.5538444 | 0.18174658 | 0.001 | 0.036 | . |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.06667255 | 1.9859527 | 0.11041687 | 0.108 | 1.000 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.05927454 | 2.3820253 | 0.12958449 | 0.026 | 0.936 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.06906280 | 2.7224602 | 0.14541146 | 0.005 | 0.180 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.11081709 | 4.0436561 | 0.20174244 | 0.002 | 0.072 |
6.3.4.3.5 Functional
func_post6 <- as.matrix(beta_q1f$S)
func_post6 <- as.dist(func_post6[rownames(func_post6) %in% samples_to_keep_post6,
colnames(func_post6) %in% samples_to_keep_post6])
betadisper(func_post6, subset_meta_post6$Population) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00018 0.000175 0.0093 999 0.926
Residuals 77 1.45162 0.018852
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.921
Hot_dry 0.92349
adonis2(func_post6 ~ type*time_point,
data = subset_meta_post6 %>% arrange(match(Tube_code,labels(func_post6))),
permutations = 999,
strata = subset_meta_post6 %>% arrange(match(Tube_code,labels(func_post6))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 8 | 0.1632318 | 0.07638509 | 0.7236452 | 0.56 |
| Residual | 70 | 1.9737271 | 0.92361491 | NA | NA |
| Total | 78 | 2.1369589 | 1.00000000 | NA | NA |
pairwise <- pairwise.adonis(func_post6, subset_meta_post6_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.0220766636 | 0.650021923 | 0.0415348890 | 0.400 | 1 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.0167496054 | 0.520911459 | 0.0335619117 | 0.448 | 1 | |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | -0.0083255553 | -0.228001097 | -0.0154346814 | 0.888 | 1 | |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.1265279179 | 3.455505193 | 0.1979607668 | 0.082 | 1 | |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.0510084290 | 1.239518381 | 0.0763272870 | 0.286 | 1 | |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.0376809680 | 1.119650309 | 0.0694587220 | 0.305 | 1 | |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.0292200956 | 0.920511083 | 0.0578191917 | 0.379 | 1 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.0133433458 | 0.270473784 | 0.0177122064 | 0.507 | 1 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.0010225901 | 0.054303886 | 0.0033825127 | 0.651 | 1 | |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.0021570675 | 0.094115687 | 0.0058478321 | 0.635 | 1 | |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.0566023632 | 2.560370692 | 0.1458039091 | 0.185 | 1 | |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.0095691236 | 0.350955208 | 0.0214638964 | 0.495 | 1 | |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | -0.0005177706 | -0.025585400 | -0.0016016487 | 0.750 | 1 | |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.0013301207 | 0.072110871 | 0.0044867082 | 0.606 | 1 | |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.0060959077 | 0.174487757 | 0.0107878382 | 0.576 | 1 | |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | -0.0017456629 | -0.082250179 | -0.0051671989 | 0.703 | 1 | |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.0577586745 | 2.845456220 | 0.1594499006 | 0.172 | 1 | |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.0055752661 | 0.218035597 | 0.0134440201 | 0.530 | 1 | |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.0010345754 | 0.055797964 | 0.0034752533 | 0.671 | 1 | |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | -0.0001056284 | -0.006306177 | -0.0003942915 | 0.693 | 1 | |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.0017235602 | 0.051851181 | 0.0032302306 | 0.746 | 1 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.1195408549 | 4.847647043 | 0.2442429086 | 0.063 | 1 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.0525878365 | 1.773089316 | 0.0997625840 | 0.206 | 1 | |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.0265995825 | 1.175418056 | 0.0684360667 | 0.335 | 1 | |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.0145818992 | 0.699759916 | 0.0419023938 | 0.420 | 1 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | -0.0080695208 | -0.216173226 | -0.0136958691 | 0.916 | 1 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.0129803540 | 0.443076619 | 0.0286909552 | 0.460 | 1 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.0267162134 | 1.225605811 | 0.0755352882 | 0.311 | 1 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.0384388433 | 1.932815819 | 0.1141461550 | 0.220 | 1 | |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.0553988290 | 1.478193905 | 0.0897060633 | 0.258 | 1 | |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | -0.0040061386 | -0.148504693 | -0.0093684974 | 0.726 | 1 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.0024023972 | 0.095389804 | 0.0059265296 | 0.612 | 1 | |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | -0.0004960759 | -0.011903277 | -0.0007445087 | 0.855 | 1 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | -0.0080428882 | -0.442986255 | -0.0284750185 | 0.859 | 1 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | -0.0011796256 | -0.034047378 | -0.0021324990 | 0.897 | 1 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.0036300838 | 0.110487573 | 0.0068581148 | 0.707 | 1 |
beta_richness_nmds_post6 <- richness_post6 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post6, by = c("sample" = "Tube_code"))
beta_neutral_nmds_post6 <- neutral_post6 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post6, by = c("sample" = "Tube_code"))
beta_phylo_nmds_post6 <- phylo_post6 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post6, by = join_by(sample == Tube_code))
beta_func_nmds_post6 <- func_post6 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post6, by = join_by(sample == Tube_code))6.3.5 5. Are there differences between the control and the treatment group?
6.3.5.1 After 1 week –> Post-FMT1
6.3.5.1.2 Richness
richness_post1 <- as.matrix(beta_q0n$S)
richness_post1 <- as.dist(richness_post1[rownames(richness_post1) %in% samples_to_keep_post1,
colnames(richness_post1) %in% samples_to_keep_post1])
betadisper(richness_post1, subset_meta_post1$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.017675 0.0088373 2.3825 999 0.105
Residuals 23 0.085312 0.0037092
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.0040000 0.668
Hot_control 0.0068795 0.213
Treatment 0.6248469 0.2084296
adonis2(richness_post1 ~ type,
data = subset_meta_post1 %>% arrange(match(Tube_code,labels(richness_post1))),
permutations = 999,
strata = subset_meta_post1 %>% arrange(match(Tube_code,labels(richness_post1))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 2 | 1.195567 | 0.1448246 | 1.947534 | 1 |
| Residual | 23 | 7.059710 | 0.8551754 | NA | NA |
| Total | 25 | 8.255277 | 1.0000000 | NA | NA |
#Arrange of metadata dataframe
subset_meta_post1_arrange <- column_to_rownames(subset_meta_post1, "Tube_code")
subset_meta_post1_arrange<-subset_meta_post1_arrange[labels(richness_post1),]
pairwise <- pairwise.adonis(richness_post1, subset_meta_post1_arrange$type, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 0.5615418 | 1.729004 | 0.1033537 | 0.020 | 0.060 | |
| Control vs Hot_control | 1 | 0.8438429 | 2.793772 | 0.1486541 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.3734921 | 1.268929 | 0.0779971 | 0.129 | 0.387 |
6.3.5.1.3 Neutral
neutral_post1 <- as.matrix(beta_q1n$S)
neutral_post1 <- as.dist(neutral_post1[rownames(neutral_post1) %in% samples_to_keep_post1,
colnames(neutral_post1) %in% samples_to_keep_post1])
betadisper(neutral_post1, subset_meta_post1$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.011001 0.0055005 0.6303 999 0.572
Residuals 23 0.200714 0.0087267
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.22500 0.955
Hot_control 0.21166 0.446
Treatment 0.95468 0.43604
adonis2(neutral_post1 ~ type,
data = subset_meta_post1 %>% arrange(match(Tube_code,labels(neutral_post1))),
permutations = 999,
strata = subset_meta_post1 %>% arrange(match(Tube_code,labels(neutral_post1))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 2 | 1.395968 | 0.1900228 | 2.697931 | 1 |
| Residual | 23 | 5.950350 | 0.8099772 | NA | NA |
| Total | 25 | 7.346318 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(neutral_post1, subset_meta_post1_arrange$type, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 0.6051778 | 2.250849 | 0.13047758 | 0.013 | 0.039 | . |
| Control vs Hot_control | 1 | 1.0528902 | 4.143637 | 0.20570451 | 0.002 | 0.006 | * |
| Treatment vs Hot_control | 1 | 0.4150076 | 1.637268 | 0.09840968 | 0.050 | 0.150 |
6.3.5.1.4 Phylogenetic
phylo_post1 <- as.matrix(beta_q1p$S)
phylo_post1 <- as.dist(phylo_post1[rownames(phylo_post1) %in% samples_to_keep_post1,
colnames(phylo_post1) %in% samples_to_keep_post1])
betadisper(phylo_post1, subset_meta_post1$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.00440 0.0021994 0.1369 999 0.908
Residuals 23 0.36941 0.0160614
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.93200 0.698
Hot_control 0.91505 0.776
Treatment 0.63312 0.73046
adonis2(phylo_post1 ~ type,
data = subset_meta_post1 %>% arrange(match(Tube_code,labels(phylo_post1))),
permutations = 999,
strata = subset_meta_post1 %>% arrange(match(Tube_code,labels(phylo_post1))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 2 | 0.07451036 | 0.07059466 | 0.8735033 | 1 |
| Residual | 23 | 0.98095695 | 0.92940534 | NA | NA |
| Total | 25 | 1.05546731 | 1.00000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 0.01842535 | 0.4144162 | 0.02688498 | 0.791 | 1.000 | |
| Control vs Hot_control | 1 | 0.05987967 | 1.7387847 | 0.09802164 | 0.122 | 0.366 | |
| Treatment vs Hot_control | 1 | 0.03212966 | 0.6477782 | 0.04139746 | 0.712 | 1.000 |
6.3.5.1.5 Functional
func_post1 <- as.matrix(beta_q1f$S)
func_post1 <- as.dist(func_post1[rownames(func_post1) %in% samples_to_keep_post1,
colnames(func_post1) %in% samples_to_keep_post1])
betadisper(func_post1, subset_meta_post1$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.00400 0.0019999 0.1431 999 0.88
Residuals 23 0.32135 0.0139717
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.61100 0.766
Hot_control 0.60188 0.845
Treatment 0.74597 0.84473
adonis2(func_post1 ~ type,
data = subset_meta_post1 %>% arrange(match(Tube_code,labels(func_post1))),
permutations = 999,
strata = subset_meta_post1 %>% arrange(match(Tube_code,labels(func_post1))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 2 | 0.1230554 | 0.1608583 | 2.204479 | 1 |
| Residual | 23 | 0.6419374 | 0.8391417 | NA | NA |
| Total | 25 | 0.7649929 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 0.11954085 | 4.8476470 | 0.24424291 | 0.086 | 0.258 | |
| Control vs Hot_control | 1 | 0.05258784 | 1.7730893 | 0.09976258 | 0.245 | 0.735 | |
| Treatment vs Hot_control | 1 | 0.01298035 | 0.4430766 | 0.02869096 | 0.452 | 1.000 |
beta_richness_nmds_post1 <- richness_post1 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post1, by = join_by(sample == Tube_code))
beta_neutral_nmds_post1 <- neutral_post1 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post1, by = join_by(sample == Tube_code))
beta_phylogenetic_nmds_post1 <- phylo_post1 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post1, by = join_by(sample == Tube_code))
beta_functional_nmds_post1 <- func_post1 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post1, by = join_by(sample == Tube_code))p0<-beta_richness_nmds_post1 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_post1 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylogenetic_nmds_post1 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")
p3<-beta_functional_nmds_post1 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
theme_classic()+
theme(legend.position="none")6.3.5.2 After 2 weeks –>Post-FMT2
post2 <- meta %>%
filter(time_point == "6_Post-FMT2")
post2.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post2))]
identical(sort(colnames(post2.counts)),sort(as.character(rownames(post2))))
post2_nmds <- sample_metadata %>%
filter(time_point == "6_Post-FMT2")6.3.5.2.2 Richness
richness_post2 <- as.matrix(beta_q0n$S)
richness_post2 <- as.dist(richness_post2[rownames(richness_post2) %in% samples_to_keep_post2,
colnames(richness_post2) %in% samples_to_keep_post2])
betadisper(richness_post2, subset_meta_post2$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.002011 0.0010056 0.1982 999 0.826
Residuals 24 0.121775 0.0050740
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.70400 0.801
Hot_control 0.67789 0.645
Treatment 0.79246 0.59820
adonis2(richness_post2 ~ type,
data = subset_meta_post2 %>% arrange(match(Tube_code,labels(richness_post2))),
permutations = 999,
strata = subset_meta_post2 %>% arrange(match(Tube_code,labels(richness_post2))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 2 | 1.504341 | 0.1967776 | 2.939822 | 1 |
| Residual | 24 | 6.140538 | 0.8032224 | NA | NA |
| Total | 26 | 7.644879 | 1.0000000 | NA | NA |
#Arrange of metadata dataframe
subset_meta_post2_arrange <- column_to_rownames(subset_meta_post2, "Tube_code")
subset_meta_post2_arrange<-subset_meta_post2_arrange[labels(richness_post2),]
pairwise <- pairwise.adonis(richness_post2, subset_meta_post2_arrange$type, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Treatment vs Control | 1 | 0.6463814 | 2.560441 | 0.1379515 | 0.002 | 0.006 | * |
| Treatment vs Hot_control | 1 | 0.4796256 | 1.916520 | 0.1069694 | 0.001 | 0.003 | * |
| Control vs Hot_control | 1 | 1.1305044 | 4.268317 | 0.2105906 | 0.001 | 0.003 | * |
6.3.5.2.3 Neutral
neutral_post2 <- as.matrix(beta_q1n$S)
neutral_post2 <- as.dist(neutral_post2[rownames(neutral_post2) %in% samples_to_keep_post2,
colnames(neutral_post2) %in% samples_to_keep_post2])
betadisper(neutral_post2, subset_meta_post2$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.008262 0.0041311 0.8024 999 0.443
Residuals 24 0.123559 0.0051483
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.44400 0.676
Hot_control 0.44675 0.238
Treatment 0.65989 0.25095
adonis2(neutral_post2 ~ type,
data = subset_meta_post2 %>% arrange(match(Tube_code,labels(neutral_post2))),
permutations = 999,
strata = subset_meta_post2 %>% arrange(match(Tube_code,labels(neutral_post2))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 2 | 1.923807 | 0.2603795 | 4.224537 | 1 |
| Residual | 24 | 5.464666 | 0.7396205 | NA | NA |
| Total | 26 | 7.388473 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(neutral_post2, subset_meta_post2_arrange$type, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Treatment vs Control | 1 | 1.0227481 | 4.648335 | 0.2251191 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.5010202 | 2.206532 | 0.1211945 | 0.002 | 0.006 | * |
| Control vs Hot_control | 1 | 1.3619424 | 5.771031 | 0.2650785 | 0.001 | 0.003 | * |
6.3.5.2.4 Phylogenetic
phylo_post2 <- as.matrix(beta_q1p$S)
phylo_post2 <- as.dist(phylo_post2[rownames(phylo_post2) %in% samples_to_keep_post2,
colnames(phylo_post2) %in% samples_to_keep_post2])
betadisper(phylo_post2, subset_meta_post2$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.000407 0.0002034 0.0487 999 0.961
Residuals 24 0.100305 0.0041794
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.93700 0.852
Hot_control 0.93765 0.757
Treatment 0.83933 0.76015
adonis2(phylo_post2 ~ type,
data = subset_meta_post2 %>% arrange(match(Tube_code,labels(phylo_post2))),
permutations = 999,
strata = subset_meta_post2 %>% arrange(match(Tube_code,labels(phylo_post2))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 2 | 0.1594363 | 0.2042241 | 3.079623 | 1 |
| Residual | 24 | 0.6212564 | 0.7957759 | NA | NA |
| Total | 26 | 0.7806927 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Treatment vs Control | 1 | 0.05927454 | 2.382025 | 0.1295845 | 0.032 | 0.096 | |
| Treatment vs Hot_control | 1 | 0.06906280 | 2.722460 | 0.1454115 | 0.005 | 0.015 | . |
| Control vs Hot_control | 1 | 0.11081709 | 4.043656 | 0.2017424 | 0.001 | 0.003 | * |
6.3.5.2.5 Functional
func_post2 <- as.matrix(beta_q1f$S)
func_post2 <- as.dist(func_post2[rownames(func_post2) %in% samples_to_keep_post2,
colnames(func_post2) %in% samples_to_keep_post2])
betadisper(func_post2, subset_meta_post2$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.01259 0.0062962 0.3249 999 0.79
Residuals 24 0.46507 0.0193778
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.53000 0.638
Hot_control 0.45381 0.774
Treatment 0.57452 0.74365
adonis2(func_post2 ~ type,
data = subset_meta_post2 %>% arrange(match(Tube_code,labels(func_post2))),
permutations = 999,
strata = subset_meta_post2 %>% arrange(match(Tube_code,labels(func_post2))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 2 | -0.003728287 | -0.005470434 | -0.06528805 | 1 |
| Residual | 24 | 0.685262325 | 1.005470434 | NA | NA |
| Total | 26 | 0.681534039 | 1.000000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Treatment vs Control | 1 | -0.008042888 | -0.44298625 | -0.028475019 | 0.829 | 1 | |
| Treatment vs Hot_control | 1 | -0.001179626 | -0.03404738 | -0.002132499 | 0.890 | 1 | |
| Control vs Hot_control | 1 | 0.003630084 | 0.11048757 | 0.006858115 | 0.693 | 1 |
beta_richness_nmds_post2 <- richness_post2 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post2, by = join_by(sample == Tube_code))
beta_neutral_nmds_post2 <- neutral_post2 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post2, by = join_by(sample == Tube_code))
beta_phylogenetic_nmds_post2 <- phylo_post2 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post2, by = join_by(sample == Tube_code))
beta_functional_nmds_post2 <- func_post2 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post2, by = join_by(sample == Tube_code))p0<-beta_richness_nmds_post2 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_post2 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylogenetic_nmds_post2 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")
p3<-beta_functional_nmds_post2 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
theme_classic()+
theme(legend.position="none")6.3.5.3 Post1 vs Post2
post5 <- meta %>%
filter(time_point == "6_Post-FMT2" | time_point == "5_Post-FMT1")
post5.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post5))]
identical(sort(colnames(post5.counts)),sort(as.character(rownames(post5))))
post5_nmds <- sample_metadata %>%
filter(time_point == "6_Post-FMT2"| time_point == "5_Post-FMT1")6.3.5.3.2 Richness
richness_post5 <- as.matrix(beta_q0n$S)
richness_post5 <- as.dist(richness_post5[rownames(richness_post5) %in% samples_to_keep_post5,
colnames(richness_post5) %in% samples_to_keep_post5])
betadisper(richness_post5, subset_meta_post5$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.01841 0.0092048 1.7364 999 0.19
Residuals 50 0.26505 0.0053010
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.037000 0.712
Hot_control 0.039117 0.227
Treatment 0.716358 0.218648
adonis2(richness_post5 ~ type*time_point,
data = subset_meta_post5 %>% arrange(match(Tube_code,labels(richness_post5))),
permutations = 999,
strata = subset_meta_post5 %>% arrange(match(Tube_code,labels(richness_post5))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 5 | 3.261916 | 0.1981463 | 2.322836 | 0.001 |
| Residual | 47 | 13.200248 | 0.8018537 | NA | NA |
| Total | 52 | 16.462164 | 1.0000000 | NA | NA |
#Arrange of metadata dataframe
subset_meta_post5_arrange <- column_to_rownames(subset_meta_post5, "Tube_code")
subset_meta_post5_arrange<-subset_meta_post5_arrange[labels(richness_post5),]
pairwise <- pairwise.adonis(richness_post5, subset_meta_post5_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.5615418 | 1.729004 | 0.10335366 | 0.015 | 0.225 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.8438429 | 2.793772 | 0.14865413 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.7628135 | 2.683925 | 0.14364890 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.3432605 | 1.148733 | 0.06698647 | 0.264 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 1.1269580 | 3.799256 | 0.19188884 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.3734921 | 1.268929 | 0.07799710 | 0.097 | 1.000 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.3571397 | 1.297184 | 0.07959561 | 0.137 | 1.000 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.7769467 | 2.670898 | 0.15114670 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.6502360 | 2.253407 | 0.13060650 | 0.002 | 0.030 | . |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.4132091 | 1.616138 | 0.09174188 | 0.012 | 0.180 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 1.0163992 | 3.760571 | 0.19030682 | 0.001 | 0.015 | . |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.2732563 | 1.019281 | 0.05988979 | 0.433 | 1.000 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.6463814 | 2.560441 | 0.13795154 | 0.001 | 0.015 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.4796256 | 1.916520 | 0.10696943 | 0.001 | 0.015 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.1305044 | 4.268317 | 0.21059061 | 0.001 | 0.015 | . |
6.3.5.3.3 Neutral
neutral_post5 <- as.matrix(beta_q1n$S)
neutral_post5 <- as.dist(neutral_post5[rownames(neutral_post5) %in% samples_to_keep_post5,
colnames(neutral_post5) %in% samples_to_keep_post5])
betadisper(neutral_post5, subset_meta_post5$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.01992 0.0099587 1.565 999 0.212
Residuals 50 0.31818 0.0063636
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.11400 0.876
Hot_control 0.10701 0.165
Treatment 0.87156 0.17449
adonis2(neutral_post5 ~ type*time_point,
data = subset_meta_post5 %>% arrange(match(Tube_code,labels(neutral_post5))),
permutations = 999,
strata = subset_meta_post5 %>% arrange(match(Tube_code,labels(neutral_post5))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 5 | 3.947979 | 0.2569798 | 3.251069 | 0.001 |
| Residual | 47 | 11.415016 | 0.7430202 | NA | NA |
| Total | 52 | 15.362995 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(neutral_post5, subset_meta_post5_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.6051778 | 2.250849 | 0.13047758 | 0.019 | 0.285 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 1.0528902 | 4.143637 | 0.20570451 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.8908158 | 3.714692 | 0.18842252 | 0.002 | 0.030 | . |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.3860927 | 1.552176 | 0.08843210 | 0.094 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 1.3122237 | 5.130273 | 0.24279254 | 0.002 | 0.030 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.4150076 | 1.637268 | 0.09840968 | 0.056 | 0.840 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.3157079 | 1.325203 | 0.08117526 | 0.152 | 1.000 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 1.0579520 | 4.270010 | 0.22158835 | 0.002 | 0.030 | . |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.7454015 | 2.920049 | 0.16294873 | 0.001 | 0.015 | . |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.4377161 | 1.942126 | 0.10824392 | 0.006 | 0.090 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 1.3766597 | 5.875279 | 0.26858075 | 0.001 | 0.015 | . |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.3176516 | 1.316137 | 0.07600637 | 0.199 | 1.000 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 1.0227481 | 4.648335 | 0.22511910 | 0.002 | 0.030 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.5010202 | 2.206532 | 0.12119453 | 0.001 | 0.015 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.3619424 | 5.771031 | 0.26507845 | 0.001 | 0.015 | . |
6.3.5.3.4 Phylogenetic
phylo_post5 <- as.matrix(beta_q1p$S)
phylo_post5 <- as.dist(phylo_post5[rownames(phylo_post5) %in% samples_to_keep_post5,
colnames(phylo_post5) %in% samples_to_keep_post5])
betadisper(phylo_post5, subset_meta_post5$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.00051 0.0002543 0.0265 999 0.973
Residuals 50 0.47996 0.0095993
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.88500 0.837
Hot_control 0.88926 0.926
Treatment 0.82391 0.91902
adonis2(phylo_post5 ~ type*time_point,
data = subset_meta_post5 %>% arrange(match(Tube_code,labels(phylo_post5))),
permutations = 999,
strata = subset_meta_post5 %>% arrange(match(Tube_code,labels(phylo_post5))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 5 | 0.3518222 | 0.180049 | 2.0641 | 0.001 |
| Residual | 47 | 1.6022134 | 0.819951 | NA | NA |
| Total | 52 | 1.9540356 | 1.000000 | NA | NA |
pairwise <- pairwise.adonis(phylo_post5, subset_meta_post5_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.01842535 | 0.4144162 | 0.02688498 | 0.768 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.05987967 | 1.7387847 | 0.09802164 | 0.134 | 1.000 | |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.07917244 | 3.0180046 | 0.15869197 | 0.010 | 0.150 | |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.04335491 | 1.5335604 | 0.08746429 | 0.196 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.10783045 | 3.7500438 | 0.18987521 | 0.002 | 0.030 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.03212966 | 0.6477782 | 0.04139746 | 0.689 | 1.000 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.06393539 | 1.5651817 | 0.09448624 | 0.131 | 1.000 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.05265949 | 1.2240203 | 0.07544494 | 0.295 | 1.000 | |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.09753501 | 2.2402429 | 0.12994265 | 0.016 | 0.240 | |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.07228545 | 2.3279593 | 0.12701683 | 0.031 | 0.465 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.11759094 | 3.5538444 | 0.18174658 | 0.001 | 0.015 | . |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.06667255 | 1.9859527 | 0.11041687 | 0.097 | 1.000 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.05927454 | 2.3820253 | 0.12958449 | 0.025 | 0.375 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.06906280 | 2.7224602 | 0.14541146 | 0.003 | 0.045 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.11081709 | 4.0436561 | 0.20174244 | 0.002 | 0.030 | . |
6.3.5.3.5 Functional
func_post5 <- as.matrix(beta_q1f$S)
func_post5 <- as.dist(func_post5[rownames(func_post5) %in% samples_to_keep_post5,
colnames(func_post5) %in% samples_to_keep_post5])
betadisper(func_post5, subset_meta_post5$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.00785 0.0039232 0.2322 999 0.818
Residuals 50 0.84483 0.0168966
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.57000 0.599
Hot_control 0.52384 0.860
Treatment 0.58787 0.85068
adonis2(func_post5 ~ type*time_point,
data = subset_meta_post5 %>% arrange(match(Tube_code,labels(func_post5))),
permutations = 999,
strata = subset_meta_post5 %>% arrange(match(Tube_code,labels(func_post5))) %>% pull(individual)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 5 | 0.1076682 | 0.07503698 | 0.7625685 | 0.499 |
| Residual | 47 | 1.3271997 | 0.92496302 | NA | NA |
| Total | 52 | 1.4348679 | 1.00000000 | NA | NA |
pairwise <- pairwise.adonis(func_post5, subset_meta_post5_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.1195408549 | 4.84764704 | 0.2442429086 | 0.072 | 1 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.0525878365 | 1.77308932 | 0.0997625840 | 0.218 | 1 | |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.0265995825 | 1.17541806 | 0.0684360667 | 0.303 | 1 | |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.0145818992 | 0.69975992 | 0.0419023938 | 0.437 | 1 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | -0.0080695208 | -0.21617323 | -0.0136958691 | 0.921 | 1 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.0129803540 | 0.44307662 | 0.0286909552 | 0.480 | 1 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.0267162134 | 1.22560581 | 0.0755352882 | 0.338 | 1 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.0384388433 | 1.93281582 | 0.1141461550 | 0.239 | 1 | |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.0553988290 | 1.47819391 | 0.0897060633 | 0.260 | 1 | |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | -0.0040061386 | -0.14850469 | -0.0093684974 | 0.735 | 1 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.0024023972 | 0.09538980 | 0.0059265296 | 0.604 | 1 | |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | -0.0004960759 | -0.01190328 | -0.0007445087 | 0.849 | 1 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | -0.0080428882 | -0.44298625 | -0.0284750185 | 0.832 | 1 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | -0.0011796256 | -0.03404738 | -0.0021324990 | 0.913 | 1 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.0036300838 | 0.11048757 | 0.0068581148 | 0.703 | 1 |
beta_richness_nmds_post5 <- richness_post5 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post5, by = join_by(sample == Tube_code))
beta_neutral_nmds_post5 <- neutral_post5 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post5, by = join_by(sample == Tube_code))
beta_phylogenetic_nmds_post5 <- phylo_post5 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post5, by = join_by(sample == Tube_code))
beta_functional_nmds_post5 <- func_post5 %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(subset_meta_post5, by = join_by(sample == Tube_code))p0<-beta_richness_nmds_post5 %>%
group_by(type, time_point) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_post5 %>%
group_by(type, time_point) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylogenetic_nmds_post5 %>%
group_by(type, time_point) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")
p3<-beta_functional_nmds_post5 %>%
group_by(type, time_point) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
theme_classic()+
theme(legend.position="none")